Rubin's "New Information will Evolve": Connect Stories to Statistics for . . . Future Events Expectations at Earlier Horizons

This the last article in a series of four.  The previous article illustrated that future investment returns through any investment horizon shorter than Warren Buffett’s “forever” are typically dominated by expectations at that horizon for a company’s long-term cash flows.  The second article illustrated that cases for future company cash flow are influenced by the cases for business drivers that the first article illustrated.

But we’re still missing something.  Now we are relying on cases for long-term cash flows expectations at earlier horizons, and those are influenced by expectations at earlier horizons for the events that drive those long-term cash flows.

Expectations for events can drive what investors put into their price-setting rationale at an investment horizon.  Typically these expectations are not certain but probabilistic. While investors typically hesitate to write probabilistic expectations explicitly because that is impractical without tools like the Bullet Point Network Platform, some investors are indeed explicit in specific domains where it is more practical because investments are dominated by only a few probabilities.  

Two of these domains are event-driven investing and biotechnology, so let’s start by looking at them and then considering how investors extend this approach into areas with more uncertainties.

Event-Driven Investing

BPN’s co-founders cut their teeth at Goldman Sachs while its Senior Partner was Robert Rubin, who expanded the firm’s merger arbitrage business into a broader “event-driven investing” style before becoming US Treasury Secretary. Interviewed in Goldman Sachs; The Culture of Success, Rubin said of this investing style:

You had to stick to your discipline and try to reduce everything to plusses and minuses and to probabilities . . . It was a high-risk business, but I’ll tell you, it did teach you to think of life in terms of probabilities instead of absolutes. You couldn’t be in that business and not internalize that probabilistic approach to life.

Event-driven investors try to pay prices explicitly based on their probability of different outcomes to uncertainties like the closing of a merger deal:

Merger arb formula.PNG

Rubin was known for his focus on how future stories might change his reduction of stories to probabilities. His Deputy Treasury Secretary Larry Summers, who later became President of Harvard University, described this focus:

Rubin ends half the meetings with, 'So we don't have to make a decision on this today, do we?' New information will evolve.

We can see this ongoing re-pricing in stock price charts of merger targets as new stories change investors’ reduction to probabilities:

CTB with annotations.PNG

Biotechnology

Like event-driven investors, some biotech investors write price targets explicitly based on their probability of the success of future events.  Typically for them, success is not deal closing but product launch with regulatory approval.

[Example of simple price formula from Kezar report by William Blair]

Certainly, to set these probabilities, investors can unpack them into probabilities for earlier events that influence final approval. There is even abundant historical data on the frequencies of success in one stage conditional on success in the previous stage.

[Smallest table from that BIO report]

But each new drug is different from the average drug in its class, so investors seek to apply experience in reducing stories to probabilities as they read and listen to the views of researchers, regulators, insurers, doctors, and patients.

[A few words on how stories evolve followed by a Chart of how price change with one or two stories annotated on it.]

But in practice, how can we anticipate ahead of time how those inputs to investors’ price setting may evolve over time so we can anticipate how their price may evolve?

Calling Odds on How Odds will Evolve

With the multiple time dimensions that we described in the previous article, we can produce cases for how those biotech investors, for example, may change their expected odds of outcomes and the timing of those outcomes.

[Illustration of odds moving over time, maybe cases first and then percentiles]

To do this realistically in practice, there are nuances to consider, including what billionaire investor George Soros terms the “reflexivity” of expectations over time, with expectations increases in one period often increasing the odds of expectations increases in the next period, and vice versa for expectations decreases.  

Atop the Bullet Point Network Platform, we have built an Event Model that considers that nuance and others, making it practical for us all to focus on our views about the eventual odds, applying the best practices detailed in our first article, and then the Event Model will help us explore cases for how expectations may change over time between what is priced in now and what eventually happens.

[Illustration entering some odds.]

Together with the Platform’s models for company long-term cash flows and valuation, as illustrated in the previous article, this Event Model enables us all to translate our insights on future events, from product launches to politics, into cases for company cash flow and valuation.

[Illustration of valuation moving over time alongside cash balance moving over time]

This can enable managers, and the investors who back them, to make better decisions about how much money to raise and to spend based on how and when valuation might change in the future as cash balance changes.  Doing this better repeatedly can make a major improvement in managers’ and investors' career success.

This all starts with organizing stories continually, as illustrated in the first article, to help us all use our human insight to question and enhance our quantitative scenarios continually.

That’s how we all can Be More Strategic by Connecting Stories to Statistics.


Damodaran's "Narrative & Numbers": Connect Stories to Statistics for . . . Long-Term Cash Expectations at Earlier Horizons

In our previous article, Wei’s “Invisible Asymptotes”: Connect Stories to Statistics for Long-Term Cash Accumulation, we illustrated that it can really pay to call odds on multiple cases for a company’s future long-term cash flows.  But as noted at the end, most of us need to do more because our limited investment horizons require that we sell our investment in any company long before all its cash flows come to us.  So going from cases for a company’s future cash flows to cases for our own investment returns requires making cases for someone else’s purchase price in the future.

That price is ultimately a human decision that can be influenced by many factors, like fear of missing out (“FOMO”), also known by finance quants as a “momentum factor.”  But whoever is holding the bag at Warren Buffett’s “forever” horizon can simply depend on the company’s long-term future cash flow, so as one steps back to gradually earlier horizons, expectations for that future long-term cash flow maintain a strong magnetism on price, despite the other price influences like FOMO.  In his book Expectations Investing, Michael Mauboussin points out:

Extensive empirical research demonstrates that the market determines the prices of stocks just as it does any other financial asset. Specifically, the studies show two relationships. First, market prices respond to changes in a company’s cash flow prospects. Second, market prices reflect long-term cash flow prospects . . . companies often need ten years of value-creating cash flows to justify their stock price. For companies with formidable competitive advantages, this period can last as long as thirty years.

Cases for Expectations

Expectations for long-term cash flow can be expressed as stories, as described by New York University Finance Professor Aswath Damodaran in his book Narrative & Numbers: The Value of Stories in Business. He offers an example of our need to think through multiple cases for these stories at our investment horizon:

In my Uber narrative, I viewed Uber as a car service company that would disrupt the existing taxi market (which I estimated to be $100 billion), expanding its growth (by attracting new users) and gaining a significant market share (10 percent). The Gurley Uber narrative was a more expansive one, where he saw Uber’s potential market as much larger (drawing in new users) and its networking effects as much stronger, leading to a higher market share . . . The valuation that I produced for Uber with the Gurley narrative was $28.7 billion, much higher than my estimate of $5.9 billion. Given that the values derived from the narratives were so different, the question, if you were an investor, boiled down to which one had a higher probability of [investors embracing it at your investment horizon].

In fact, MIT Finance Professor Andrew Lo, whose finance research relies heavily on brain research, describes this anticipation of others’ narratives as the lynchpin of anything approaching market efficiency. In his book Adaptive Markets: Financial Evolution at the Speed of Thought, he explains:

The price-discovery process in a well-functioning market requires its participants to engage in a certain degree of cause-and-effect reasoning. “If I do this, then others will do that, in which case I’ll respond by …” This chain of logic presumes that individuals have what psychologists call a theory of mind, the ability to understand another person’s mental state.

And we might have multiple investment horizons that matter to us.  We may have an expectation that we are confident will make us money within 3 years since the market’s expectation is likely to evolve to ours by then, but it may matter to us what the market’s expectation is in 1 year or 1 quarter because our clients might take their money back if the investment loses over those earlier horizons, robbing us of the chance to win over 3 years even if we were right.

So our added task is to step back from cases for long-term future cash flows to cases for expectations for long-term future cash flows at multiple earlier horizons.  This means 2 time dimensions:  

  1. What eventually happens over future time periods, and

  2. What investors expect for future time periods at each period before they move from future (yellow below) to past (green below).  

2 time dimensions.PNG

Mind blown?  Too hard? When we all reflect, we may note that we do this in our heads, and that is easier than making it explicit in spreadsheets and memos and slide decks, where extra dimensions like this are hard.  But there are advantages to making expectations explicit, which is why managers and investors write spreadsheets and memos and slide decks, and the Bullet Point Network Platform makes it practical to make explicit not only one case for future long-term cash flows but also many cases for future long-term cash flows and even for earlier expectations about that long-term.  That enables us all to amplify what’s in our heads, catching oversights and increasing confidence in our foresight.

We need to think through how cases for each driver from our first article may go from what is priced in today to the cases for what eventually happens.  That involves thinking through serial correlation as well as what Economics Nobel Laureate Daniel Kahneman calls “temporal scope sensitivity.”  The Bullet Point Network Platform includes models for these so you do not have to focus on them but instead can continue to focus on . . .

  1. What investors expect now, and

  2. What are the odds for what will eventually happen

The Platform help us all translate our views on those two points into cases for expectations between those points, which is essential to getting paid within our practical investment horizons.

[EXAMPLE FROM BPN OF CHANGES IN EXPECTATIONS FOR DRIVERS]

[EXAMPLE OF CHANGES IN EXPECTATIONS FOR LONG-TERM CASH FLOWS]

Cases for the Price Driven by those Expectations

Wait a minute, aren’t we missing something?  How do the expectations at any horizon for long-term cash flows translate into a price at that point?  Is it via a multiple of in year?

There are many ways to fill in those blanks, most of them easy to do in measuring a past price in a past year.  But how do we put odds on cases for how to fill in those blanks at future horizons, especially for growth companies whose outlooks are harder to compare to any real “comparables” than those of more stable, slow-growth companies?  For a price at the 12/31/2023 horizon, why 10x 2024 revenue instead of 10x 2023 revenue? Or instead of 2x 2023 revenue? And do the answers change in cases wherein inflation expectations are 3% annually instead of 2% annually? Or if 10-year Treasury rates are 1% or 5%?

Many articles have been written about how to apply the most universally non-negative multiple, the revenue multiple, to the growthiest of growth companies, tech startups, but most of those articles simply pick today’s revenue multiple for a narrow range of deals and justify it with arbitrary math, claiming without justification that this multiple can be used for more deals at more times.  A more sincere review of revenue multiples for tech startups reveals giant ranges from 1x to over 600x, and some of the huge differences are between the same company at different times, such as Facebook ranging from under 10x to over 100x.  Another objective article illustrates that even just in a single year, just among private “Unicorns” valued at $1B or more, revenue multiples ranged from 1x to 136x.

Peter Fenton, Bill Gurley’s Benchmark partner and fellow top-ten member of the 2018 Midas List, has been quoted as saying, “Never turn down a company based on valuation, because it is a mental trap.” When asked to explain this, he detailed that valuation is counterproductive when it is based on arbitrary multiples but is actually useful when it is focused instead on the odds of “radical full potential” in the long-term:

There’s probabilistic outcomes and future world states that could occur for every investment . . . You have to be on the field practicing every day, working with companies that have the potential that may never get realized and then intersecting, when all the stars align, the companies where the potential is fully realized . . . What strikes me is that [short-term multiples valuation] thinking so dominates the trade . . . that it sabotages this underlying question of what does it look like in radical full potential, and I think that partners, the venture capitalists that are best suited to work with the company, have to believe in that but also be practical and realistic that there’s a lot of possible future world states and so you have to mitigate that by paying a price that encompasses those potential outcomes.

So how can we all pay a price that encompasses those potential outcomes? Fortunately, the Bullet Point Network Platform can help us all explain consistently when, for example, the odds of long-term full potential might drive investors to pay 10x 2024 revenue at a 12/31/2023 investment horizon instead of 10x 2023 revenue or 2x 2023 revenue.

The least arbitrary of multiples, cash flow multiples, are the reciprocal of what real estate investors like to call the “cap rate,” which can be calculated from cost of capital minus expected future growth rate into perpetuity.  The latter term in that calculation is the really vexing one, because it is influenced not just by current annual growth but moreso by the long-term asymptotes emphasized by Wei and Gurley. The key is forecasting expectations for those long-term asymptotes.  We have that key, as we showed in our previous article, Wei’s “Invisible Asymptotes”: Connect Stories to Statistics for Long-Term Cash Accumulation.

So we can predict cases at each future investment horizon for many multiples of many metrics in many years, by:

  • Predicting cases for long-term cash flow expectations at each future investment horizon

  • Putting an “exit multiple” on cash flow only at the long-term asymptotes that end the expectations’ S-Curves.  As Michael Mauboussin explains in Expectations Investing, a company’s cash flow may grow only with inflation beyond those long-term asymptotes, making the cap rate’s “expected future growth rate into perpetuity” term no longer vexing but instead simply the expected inflation rate, for which it is much easier to predict cases.

  • Predicting cases for investors’ opportunity costs, aka “cost of capital”

From these ingredients, we can all produce realistic and non-arbitrary cases, at each investment horizon, for the blanks in multiple of in year, and these cases for multiples correspond to the cases for the long-term cash flow expectations, including the asymptotes, that they value.

Predicting a single case for cost of capital is easier than predicting a single case for multiple of in year, and predicting the odds of multiple cases for cost of capital loosens the reins further.  The Bullet Point Network Platform can make it easy for all of us, since the Platform reflects its creators’ decades experiencing the capital markets at senior levels, over a decade of developing patented software architecture and building capital markets models atop it to simulate that experience, and ongoing daily work by the Bullet Point Network research team connecting statistical base rates with potential changes suggested by stories in the daily news, providing the continuous and incremental updating that Tetlock advocates.

[EXAMPLE OF CASES FOR VARIOUS CAPITAL MARKETS RATES CHANGING TOGETHER OVER TIME]

This infrastructure enables us all to focus on our insights about business drivers, which the Platform help us to translate into realistic scenarios for company cash flows, for investors’ future expectations about those cash flows, and for investors’ future prices for our investments.

[EXAMPLE OF CASES FOR COST OF CAPITAL at that 1 horizon, for revenue and cash flow expectations at that 1 horizon, for P/E, EV / EBITDA, AND EV / REVENUE at that 1 horizon, and for price at that 1 horizon]

Often, this depends not only on the odds of events, as described in our first article, but also on anticipating how investors’ expectations for those odds may change over time as milestones approach. The Bullet Point Network Platform provides powerful help with that specifically, which we will detail in our last article, Rubin’s “New Information will Evolve”: Connect Stories to Statistics for Future Events Expectations at Earlier Horizons.

Wei’s "Invisible Asymptotes": Connect Stories to Statistics for . . . Long-Term Cash Accumulation

Benchmark Capital co-founder Bill Gurley recommended as “iconic” for “its lucidity, applicability, and therefore overall usefulness,” the concept of “Invisible Asymptotes” described by former Amazon strategic planner Eugene Wei:

It didn't take long for me to see that our visibility out a few months, quarters, and even a year was really accurate (and precise!). What was more of a puzzle, though, was the long-term outlook. Every successful business goes through the famous S-curve, and most companies, and their investors, spend a lot of time looking for that inflection point towards hockey-stick growth. But just as important, and perhaps less well studied, is that unhappy point later in the S-curve, when you hit a shoulder and experience a flattening of growth.

By producing many cases for events as illustrated in our previous article, Tetlock’s “Superforecasting”: Connect Stories to Statistics for Future Events, we can drive cases for business model characteristics’ long-term “Asymptotes,” such as:

  • Peak market unit volume

  • Peak market share

  • Unit price

  • Peak margins

  • Incremental margins

  • Working capital requirements

  • Capital spending requirements

The Bullet Point Network Platform includes a Curve Model with intelligence built in to interpolate realistically from different cases for each characteristic’s long-term asymptote into different cases for its value over all intervening periods, such as years or quarters.

[ILLUSTRATE CURVES FOR 10 CASES]

The Platform also includes an Integrated Financial Statement Model with intelligence built in to flex appropriately for different cases for these characteristics in order to produce cases for cash flow in future periods. This makes financial modeling flexible enough to amplify rather than hamstring the strategic thinking prized by managers like Wei and investors like Gurley.

[ILLUSTRATE 10 CASES FOR REVENUE AND CASH FLOW TOGETHER--INCREASING REVENUE MAY PRODUCE DECREASING CASH FLOW AND THEN SLOWING REVENUE GROWTH MAY PRODUCE INCREASING CASH FLOW]

In limited situations, cases for a company’s long-term cash flows are enough to serve as cases for our long-term return in an investment in the company, such as when . . .

  • Near-term annual cash flow is very high relative to the investment’s price, typically when investors expect that cash flow will crater soon, otherwise known as a “distressed” situation.

  • We have the luxury that our horizon is Warren Buffett’s favorite holding period of “forever”.

On average over the long-term, distressed investors and long-horizon investors have produced more profits than short-term investors, suggesting that it can be very advantageous to have the luxury to operate in these limited situations. Of the hundred wealthiest people in the world as listed by Forbes, only two—Jim Simons and Ray Dalio—are short-term investors, and even those two have made major investments in information systems that they did not sell in the short-term but instead may rely on to help them produce cash flow “forever.”

But hold on.  Distressed high yield situations are few in today’s economic environment, and few of us have the luxury of a horizon as long as Warren Buffet’s “forever.”  Usually we do not operate in these limited situations, so our future returns on an investment are dominated not by its cash flows but by the price at which we can sell it to someone else within our investment horizon.  In those more typical situations, the Bullet Point Network Platform can help us all even more, as we illustrate in our next article, Damodaran’s “Narrative & Numbers”: Connect Stories to Statistics for Long-Term Cash Expectations at Earlier Horizons.

Tetlock's "Superforecasting": Connect Stories to Statistics for . . . Future Events

How can statistics for future events help us all improve our decisions?

Our long-term results will be the sum of our results from many decisions whose outcome is impossible to predict with certainty.  Some of these decisions will produce winning outcomes and some will producing losing outcomes, so improving our long-term results relies on increasing the number of winning outcomes and their respective amounts and reducing the number of losing outcomes and their respective amounts.  

Thanks to that simple math, even though we cannot predict each outcome with certainty, we can all improve our long-term results by predicting the odds of each outcome.

How can stories help us all produce better statistics for future events?

Statistics from the past offer a good start, since past odds are observable as frequencies, and many events do repeat (like daily temperature). Old statistical methods like regression analysis can help us extrapolate some of these repeat patterns, and newer methods like deep neural networks can help us extrapolate even more of them.

But billionaire investor and author Ray Dalio discourages us from extrapolating past data blindly:

The main thrust of machine learning in recent years has gone in the direction of data mining, in which powerful computers ingest massive amounts of data and look for patterns.  While this approach is popular, it’s risky in cases when the future might be different than the past. Investment systems built on machine learning that is not accompanied by deep understanding are dangerous because when some decision rule is widely believed, it becomes widely used, which affects the price.  In other words, the value of a widely known insight disappears over time. Without deep understanding, you won’t know if what happened in the past is genuinely of value and, even if it was, you will not be able to know whether or not its value has disappeared—or worse. It’s common for some decision rules to become so popular that they push the price far enough that it becomes smarter to do the opposite.

Fortunately, in his cogent book Superforecasting: The Art and Science of Prediction, University of Pennsylvania Psychology Professor Philip Tetlock, the very coiner of the phrase “The average expert was roughly as accurate as a dart-throwing chimpanzee,” encourages us all that we can indeed improve our future statistics by supplementing past data with Dalio’s “deep understanding” through stories:

I realized that as word of my work spread, its apparent meaning was mutating. What my research had shown was that the average expert had done little better than guessing . . . But debunkers go too far when they dismiss all forecasting as a fool’s errand. I believe it is possible to see into the future, at least in some situations and to some extent, and that any intelligent, open-minded, and hardworking person can cultivate the requisite skills.

And Tetlock’s team proved this in its Good Judgment Project, which calculated a Brier score for explicit odds forecasts by over 20,000 people for over 500 future events requested by the CIA’s research arm. It found that the best group of people were 66% more accurate than random guesses through disciplined connection of news stories to statistics. Take that, dart-throwing chimpanzees!

Tetlock offers 10 best practices to join that best group. Notably, Tetlock’s advice there is reinforced by none other than Ray Dalio, whose best-selling book Principles recommends 11 similar best practices for decision-making.

Below, we illustrate each of Dalio’s best practices by using the Bullet Point Network Platform to connect stories to statistics for a future event involving a fantasy company that we call virtualgoods.shop. Any resemblance to actual companies, living or dead, or actual events is purely coincidental:

1. Recognize that decision-making is a two-step process (first learning and then deciding)

Our patented architecture’s Logical Graphical Model helps with the learning step by helping us all connect excerpts from stories more scalably than we can with folders and tags, and its Probabilistic Graphical Model helps with the deciding step by helping us all translate those stories into explicit odds that we can weigh.

Logical Graphical Models use Description Logics to infer some qualitative relationships (the dotted lines) from others, helping us draw more connections between excerpts from stories.

LGM for esports.PNG

Probabilistic Graphical Models use Bayesian Probability to infer odds of scenarios for some Issues (dotted green and orange) from scenarios for other Issues (yellow) and the conditionality between them (blue and red).

PGM for esports.PNG

2. Synthesize the situation at hand

When we all identify an important issue and some of its potential outcomes, our Platform’s architecture encourages us all to synthesize data and stories about those outcome from multiple sources and to seek arguments in favor of each of the potential outcomes in order to call their odds more accurately, as you can see below, where the argument from AE, Inc. may carry the most weight in our minds even though it is not the most recent.

vgs first market.PNG

3. Synthesize the situation over time

4. Navigate levels effectively

5. Logic, reason, and common sense are your best tools for synthesizing reality and understanding what to do about it

6. Make your decisions as expected value calculations

7. Prioritize by weighting the value of additional information against the cost of not deciding

8. Simplify!

9. Use principles

10. Believability weight your decision making

11. Convert your principles into algorithms and have the computer make decisions alongside you

Wouldn’t cases for events like these be even more useful to a businesspeople like us if they help us produce cases for a company’s future cash flows?  The Bullet Point Network Platform can help us all even more with that, as we illustrate in our next article, Wei’s “Invisible Asymptotes”: Connect Stories to Statistics for Long-Term Cash Accumulation.

Bullet Point Network: A New Model For Investment & Beyond

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Bullet Point Network helps venture investors and early-stage portfolio companies be more strategic by connecting stories to statistics. We build models, provide insight, and support your decision-making process.

Paired with a team of fundamental analysts, we use our patented graph database and dozens of frameworks that we’ve used to model hundreds of companies to give you a full picture on any company. We start with your knowledge and then include all the insight you can get from our networks, like industry expert interviews and natural language processing (NLP) tools. Plus, we use what you and your team gather every day — giving you a deeper, more robust understanding of where a company’s future lies.

Our process is fundamentally simple: We believe making a call on the long-term future of early-stage companies is best done by humans. But we also believe in the power of going beyond qualitative views on TEAM, total addressable market (TAM), and product differentiation. To do that, we support human research and a thorough decision-making process powered by BPN’s patented logical and probabilistic models, which we use to organize research, map evidence to your key drivers, and produce probability-based scenarios for what may unfold. We also keep each company’s model continually updated with new information, allowing us to measure how the company tracks relative to milestones over time.

To think about it conceptually, imagine a spreadsheet where right next to the cell for a key assumption — such as TAM, unit pricing, or margins — you have all the evidence that supports a specific judgment. This is exactly where we start.

Next, we connect related issues and map influence relationships. By tracking how X influences Y and Z, our proprietary framework allows for any changes to our judgment on X to flow directly to Y and Z, just like they would in a real-life decision-making scenario.

Using YOUR insight and logic — not a random Monte Carlo analysis based on past data alone — we build scenarios for each key driver based on evidence and judgments. Then we connect the drivers together in an integrated financial model showing future cash flows.

With our models, we can literally run hundreds of logical combinations on all key drivers and see the results of hundreds of scenarios showing what cash flows might result. The best part? Each scenario is built on sound logic and includes the probability that the scenario will occur.

When it comes to the specifics, we often zero in on 5-7 representative cases to see in more detail what is most likely, most attractive, or most risky.  With this insight, we support our investment decision on walk-away price and sizing. For the portfolio company, we deliver a framework to map new evidence, to make strategic operational decisions on key drivers, and to really understand how the company is tracking relative to milestones over time. And with that, we’re able to help deliver insight to raise new rounds of capital or to start the conversation about exit values.

We have dozens of proprietary frameworks and industry models, to which we add YOUR knowledge about company-specific drivers so YOU can make better decisions. Naturally, we focus a lot on cash burn and equity upside, and we prefer to work with companies who can fund enough runway to validate specific milestones.

To understand how BPN ties together all the knowledge you have on a company to support better decision making, check out our video below:

But we’re more than just a platform, and that’s what makes us unique. We have frameworks for all kinds of companies: pre-FDA biotechs, promising pre-revenue B2B companies, B2C platforms with customer traction who need to scale, accelerating growth stage companies, companies approaching peak market share, companies in almost every stage and segment, but we also have analysts. Those analysts are an extension of your team — helping with research and modeling — and bolstering our proprietary software platform to support every kind of investment and portfolio company.

We often model multiple dimensions — like sequential product rollouts, regional market penetration, and alternative distribution channels — and we always look at multiple time horizons. For example: While we do model the ACTUAL probabilistic cash flows over long time horizons (like 2030 or 2050), we have a proprietary framework to estimate how expectations for those future cash flows may change over shorter time frames – like when you raise your next round in 18 months or look to exit in 5 years – always based on tracking key drivers and reaching important milestones.

These are things venture investors and company CEOs talk about all the time. With Bullet Point Network, you can harness that knowledge and quantify your key judgments to analyze the likelihood and impact of potential scenarios occurring.

Want to learn more about BPN’s strategy and how it could work for you? Get in touch with our team to get more insight into our process.